Method of predicting blood glucose level using near-infrared (NIR) spectroscopy data

ABSTRACT

A method for predicting a blood glucose level using a near-Infrared (NIR) spectrometer is provided. The method may include obtaining a feature set from an NIR glucose spectra; and predicting glucose values from the feature set based on a binary classification of the NIR glucose spectra and an in-class prediction of glucose using Machine Learning Regression.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from Indian Patent Application No.201741015911, filed on May 5, 2017 in the Indian Patent Office, thedisclosure of which is incorporated herein by reference in its entirety.

BACKGROUND 1. Field

Apparatuses and methods consistent with exemplary embodiments relate toglucose monitoring, and more particularly relate to predicting a bloodglucose level using Near-Infrared (NIR) Spectroscopy data.

2. Description of the Related Art

Glucose monitoring may be used to measure the level of glucose in ablood sample. The glucose monitoring may be performed either invasivelyor non-invasively. In the invasive method, the skin of a person ispierced to obtain the blood sample. In the non-invasive method,collection of the blood sample may not be required to measure theglucose level, and instead Mid-Infrared (Mid-IR) spectroscopy,Near-Infrared (NIR) spectroscopy, or Raman spectroscopy may be used. TheNIR spectroscopy has been used for continuous glucose monitoring, inwhich NIR waves are generated to pass through the skin and a spectrumindicating absorption of the NIR waves by the blood underneath the skinis used in determining the glucose level. The absorption of the NIRwaves is defined by BEER-Lambert law:

$A = {{\log\left( \frac{I}{I_{0}} \right)} = {\epsilon\;{Cd}}}$

-   Where ϵ is an absorption co-efficient,-   C is a concentration of a component in sample, and-   d is a penetration depth.

If the sample includes different constituents, then the overallabsorption is obtained based on the following equation:A=ϵ_1C_1d+ϵ_2C_2d+ . . . +ϵ_nC_nd

The NIR absorption spectrum indicates absorption of several componentssuch as water, fat, protein (Collagen and Keratin), Amino acids, elastinand Glucose. Therefore,A_NIR=A_Water+A_Cholesterol+A_Collagen+A_Keratin+A_elastin+A_acid+A_Glucose

Further, the concentration of glucose in an interstitial liquid is givenbyC_Glucose=A_Glucose/A_Water

Thus, glucose monitoring is very challenging as the values of glucoseabsorption is of several orders lesser than other constituents and manytimes, the glucose information is distorted due to the noise componentsof the NIR data. The order of concentration of different constituentsare shown in the below table.

Constituent Water Fat Protein Elastin/Acid Glucose Order of10{circumflex over ( )}0 10{circumflex over ( )}−1 10{circumflex over( )}−3 10{circumflex over ( )}−3 10{circumflex over ( )}−4 concentration(~)

Therefore, there is a need for a method of predicting blood glucosevalues with a high accuracy using NIR spectroscopy data.

SUMMARY

Exemplary embodiments address at least the above problems and/ordisadvantages and other disadvantages not described above. Also, theexemplary embodiments are not required to overcome the disadvantagesdescribed above, and may not overcome any of the problems describedabove.

One or more exemplary embodiments provide a method of predicting bloodglucose level using Near-Infrared (NIR) spectroscopy data.

According to an aspect of an exemplary embodiment, there is provided amethod of predicting a blood glucose level using near-infrared (NIR)spectroscopy data. The method may include: obtaining a feature set froman NIR glucose spectra; and predicting glucose values from the featureset based on a binary classification of the NIR glucose spectra and anin-class prediction of glucose using Machine Learning Regression.

The obtaining the feature set may include: obtaining a raw feature setfrom NIR glucose spectra samples associated with different blood glucoselevels; identifying one or more glucose dependent features present inthe NIR glucose spectra samples; and removing collinearity from theidentified one or more glucose dependent features to obtain the featureset.

The identifying one or more glucose dependent features present in theNIR glucose spectra samples may include: obtaining a low variance set offeatures that exhibits a low variance for a same glucose value in theNIR glucose spectra; obtaining a high variance set of features thatvaries in accordance with a change in glucose levels; and obtaining theone or more glucose dependent features as features which are common toboth of the low variance set and the high variance set.

The predicting the glucose values may include: obtaining a glucose plotrepresenting a plurality of glucose values at different time instances;and classifying the plurality of glucose values based on the timeinstances into a first bin and a second bin, wherein the first bin maycorrespond to glucose values with a rise time period and the second binmay correspond to glucose values with a decay time period.

The method may further include: generating an artificial neural network(ANN) framework based a classification model that classifies the NIRglucose spectra into a first bin and a second bin using abackpropagation method; and classifying the NIR glucose spectra based onthe generated classification model.

The predicting the glucose values may include: segregating the NIRspectroscopy data into one or more binary classes; generating aregression model for each of the one or more binary classes; andpredicting the glucose values based on the generated regression modelfor each class.

According to an aspect of another exemplary embodiment, there isprovided a glucose prediction device including at least one or moreprocessors. The at least one or more processors may include: a featureset extraction unit configured to obtain a feature set from an NIRglucose spectra; and a prediction unit configured to predict glucosevalues from the feature set based on a binary classification of the NIRglucose spectra and an in-class prediction of glucose using MachineLearning Regression.

The feature set extraction unit may include: a raw feature extractionunit configured to extract a raw feature set from NIR glucose spectrasamples associated with different blood glucose levels; a glucosedependent features isolation unit configured to identify one or moreglucose dependent features present in the NIR glucose spectra samples;and a collinearity removal unit configured to remove collinearity fromthe identified one or more glucose dependent features to obtain thefeature set.

The glucose dependent features isolation unit may be further configuredto obtain a low variance set of features that represents a low variancefor a same glucose value in the NIR glucose spectra, obtain a highvariance set of features that varies in accordance with a change inglucose levels, and obtain the one or more glucose dependent features asfeatures which are common to both of the low variance set and the highvariance set.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent by describingcertain exemplary embodiments, with reference to the accompanyingdrawings, in which:

FIG. 1 illustrates a preprocessed glucose spectrum for a glucose valueof 140 mg/dL, according to a comparative example.

FIG. 2 illustrates different glucose spectra obtained for differentlevels of glucose values, according to an exemplary embodiment.

FIG. 3 is a flow chart illustrating a method of isolating glucosedependent features from a raw feature set, according to an exemplaryembodiment.

FIG. 4 is a flowchart illustrating a method of removing collinearityfrom extracted glucose dependent features, according to an exemplaryembodiment.

FIG. 5 is a block diagram illustrating various components of a glucoseprediction unit, according to an exemplary embodiment.

FIG. 6A illustrate variations in glucose values with respect to time ofa test subject, according to an exemplary embodiment.

FIG. 6B shows spectra associated with a glucose value of 141 mg/dL atdifferent instances in a glucose plot, according to an exemplaryembodiment.

FIG. 7 is a flowchart illustrating an exemplary generation of regressionmodel for predicting glucose values, according to an exemplaryembodiment.

DETAILED DESCRIPTION

Exemplary embodiments are described in greater detail below withreference to the accompanying drawings.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of the exemplaryembodiments. However, it is apparent that the exemplary embodiments canbe practiced without those specifically defined matters. Also,well-known functions or constructions are not described in detail sincethey would obscure the description with unnecessary detail.

The specification may refer to “an”, “one” or “some” embodiment(s) inseveral locations. This does not necessarily imply that each suchreference is to the same embodiment(s), or that the feature only appliesto a single embodiment. Single features of different embodiments mayalso be combined to provide other embodiments.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless expressly stated otherwise. Itwill be further understood that the terms “includes”, “comprises”,“including” and/or “comprising” when used in this specification, specifythe presence of stated features, integers, steps, operations, elementsand/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations and arrangements of one or more of theassociated listed items.

Expressions such as “at least one of,” when preceding a list ofelements, modify the entire list of elements and do not modify theindividual elements of the list.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains. It willbe further understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

Throughout the specification, the terms “bins” and “classes” areinterchangeably used.

One or more exemplary embodiments provide a method for predicting bloodglucose level using Near-Infrared (NIR) spectroscopy data. The methodincludes calculating a blood-glucose level non-invasively based on theNIR spectroscopy data. A prediction method may be used to measure theblood glucose level. The prediction method may include a two-stagealgorithm for predicting the blood glucose level.

The prediction method is explained herein in detail. Firstly, the bloodglucose level is obtained using a standard invasive procedure. Then, anon-invasive spectral scan is performed on a person using near-Infraredspectrometer to obtain raw NIR spectra. The raw NIR spectra is labelledby the glucose level which is obtained from the invasive procedure. Theobtained raw NIR spectra is preprocessed further to obtain glucosespectra. An exemplary preprocessed glucose spectra for a glucose valueof 140 mg/dL is shown in FIG. 1. The obtained glucose spectra maycontain 129 samples which are referred to as features. The glucosespectra and the associated glucose values may be arranged into the formof the following matrix X, which is referred as data matrix.

$X = \begin{bmatrix}g^{1} & x_{0}^{1} & x_{1}^{1} & \ldots & x_{129}^{1} \\g^{2} & x_{0}^{2} & x_{1}^{2} & \ldots & x_{129}^{2} \\\; & \; & \ldots & \; & \; \\g^{N} & x_{0}^{N} & x_{1}^{N} & \ldots & x_{129}^{N}\end{bmatrix}$

Here, element g^(k) is a glucose value associated with k^(th) spectrumand includes 129 samples x₀ ^(k) x₁ ^(k) . . . x₁₂₈ ^(k). N≈200 is thetotal number of samples obtained per day, the samples being obtainedconsecutively every minute till N samples are accumulated.

Further, glucose spectra for different levels of glucose values such asfor 102 mg/dL, 140 mg/dL, 190 mg/dL are obtained and shown in FIG. 2. Asshown in FIG. 2, variations in the spectra from one glucose value toanother occurs primarily around edges of the spectra. The followingfeatures are arrived at to capture the information at the edges.

-   -   Peak to valley ratio: The peak to valley ratio R_pv is defined        as the ratio of the mean of values at the peaks to the mean of        the values at the valleys.    -   Peak to peak ratio: The peak to peak ratio R_(pp)) is defined as        the ratio of values at different peaks in the spectrum waveform.    -   Mean peak value: The mean peak value μ_(p) is obtained as the        mean of values occurring at a subset of all peaks.    -   Spectra energy (E_(s)): The spectra energy E_(s) represents the        energy of a signal and it is defined as E_(s)=Σ_(n) s_(n) ². The        first two features Peak to valley ratio and to peak to peak        ratio features capture relative information in the peaks/valleys        which is expected to provide a desired output at different        signal-to-noise ratios (SNRs) while the latter features        correspond to an absolute span of a given spectra.

Spectra samples: Apart from the above four features, the 129 sample ofglucose spectra are called as spectra samples. Therefore, the total of133 features including 129 features of NIR spectra and four featuresdescribed above constitutes the raw feature set for model training.

After obtaining the raw feature set, glucose dependent features may beobtained from the set of 133 features. Among the 133 features, a fewfeatures exhibit low variance for the same glucose value in the spectraldata set and a few features vary highly with the change in glucosevalues. These features are referred to as low variance features and highvariance features, respectively. Now, from both of the low variance andhigh variance features, a set of glucose dependent features which arecommon to both of the low variance set and high variance set areselected as the glucose dependent features. The corresponding methodsteps are illustrated in FIG. 3.

FIG. 3 is a flow chart illustrating a method of isolating glucosedependent features from a raw feature set, according to an exemplaryembodiment. Firstly, the raw feature set of 133 features are classifiedinto a high variance set S_(hv) and a low variance set S_(lv). Inoperation 302, spectral data matrix X is inputted, which includes a sizeN_(Iv) of the low variance set S_(lv) and a size N_(hv) of the highvariance set S_(hv). In operation 304, the low variance set S_(lv) ofthe size N_(lv) is obtained by isolating N_(Iv) features that show leastvariations for the same glucose value in the spectral data set. Inoperation 306, the high variance set S_(hv) of the size N_(hv) isobtained by isolating N_(hv) features show maximum variations fordifferent values of glucose. In operation 308, features that are commonto both variance sets S_(lv) and S_(hv) are selected to identify glucosedependent features.

FIG. 4 is a flowchart illustrating a method of removing collinearityfrom the glucose dependent features, according to an exemplaryembodiment. The step by step procedure of removing the collinearity isexplained in detail herein. In an exemplary embodiment, the glucosedependent features may have highly correlated or collinear featureswhich tend to affect the accuracy of the glucose prediction algorithm.Thus, it may be necessary to remove collinear features from the glucosedependent features.

In operation 402, a matrix F is formed based on glucose dependentfeatures with k=1 and T_(c), where variable ‘k’ is used to index the kthrow of F and T_(c) refers to correlation threshold. In operation 404, acovariance matrix for the glucose dependent features F_(cov) isobtained. In an exemplary embodiment, the F_(cov) is calculated based onthe following formulaF _(cov) =F*F′

In operation 406, a covariance matrix for a ‘k’ th row F_(cov) ^(k) isobtained, and the indices of elements in the covariance matrix F^(k)_(cov) with magnitude greater than T_(c) are identified. Then, inoperation 408, the corresponding rows whose magnitude greater than T_(c)are removed to obtain F_(red). F_(red) is the matrix obtained frommatrix F by removing the features with a high correlation at each step.Further, in operation 408, the covariance matrix F_(cov) for F_(red) isalso computed. In operation 410, it is determined whether all the rowsin the matrix are processed. If all the rows are not processed, thevalue of k is increased by 1 until processing of all the rows getscompleted in operation 414. If all the rows are processed, in operation412, final output features that are retained in F_(red) are obtained.

These final output features that remain after the collinearity removalfrom the glucose dependent features are referred to as the feature set.Using the obtained feature set, the NIR glucose spectra is firstclassified using binary classification method. In this binaryclassification method, glucose plots for different glucose values atdifferent time instances are considered.

FIG. 5 is a block diagram illustrating one or more components of aglucose prediction unit, according to an exemplary embodiment. As shownin FIG. 5, the glucose prediction unit 500 includes a feature setextraction unit 502, and a prediction unit 504 that predicts a bloodglucose level. The feature set extraction unit 502 may include a rawfeature extraction unit 506, a glucose dependent features isolation unit508, and a collinearity removal unit 510. The prediction unit 504 mayinclude a binary classification model generation module 512 and anin-class regression model generation module 514. The raw featureextraction unit 506, the glucose dependent features isolation unit 508,the collinearity removal unit 510, the binary classification modelgeneration module 512, and the in-class regression model generationmodule 514 may be implemented with one or more processors.

The feature set extraction unit 502 may obtain a final set of glucosefeatures for predicting blood glucose values. The feature set isobtained with the use of modules including the raw feature extractionunit 506, the glucose dependent features isolation unit 508 and thecollinearity removal unit 510. The raw feature extraction unit 506 mayobtain NIR spectra of a person through NIR spectrometer. The NIR spectraare then processed by the raw feature extraction unit 506 to obtainglucose spectra. The raw feature extraction unit 506 extracts 129features from the obtained glucose spectra. The obtained glucose spectrafor different glucose values are then processed by the raw featureextraction unit to obtain four more features, amounting to a total of133 features. These 133 features are further analyzed by the glucosedependent features isolation unit 508 to extract only glucose dependentfeatures.

The glucose dependent features isolation unit 508 first segregates thewhole 133 features into a high variance set and a low variance set. Thefeatures which exhibit a low variance for the same glucose value in thespectral data set are segregated as a low variance set. The featureswhich vary highly with the change in the glucose values are segregatedas a high variance set. The glucose dependent features isolation unit508 further identifies common features present in both the high varianceand low variance data set. The identified common features are consideredas glucose dependent features.

The collinearity removal unit 510 may remove collinearity from theglucose dependent features. As shown in FIG. 4, the collinearity removalunit 510 forms a matrix using glucose dependent features. Then,covariance matrix for the ‘k’ th row is also obtained. In that ‘k’throw, indices of elements whose magnitude greater than threshold areidentified and then removed to obtain the final output features that areretained in the F_(red). The final output features are referred as‘feature set’. This ‘feature set’ is then transferred to a predictionunit for further processing.

In an exemplary embodiment, the prediction unit 504 uses the ‘featureset’ obtained from the feature set extraction unit 502 to generate amodel to predict glucose values. The prediction unit 504 deploys atwo-stage algorithm to predict a blood glucose value corresponding to agiven glucose spectra. The two-stage algorithm includes a binaryclassification model generation module 512 and an in-class regressionmodel generation module 514. The binary classification model generationmodule 512 performs training of an artificial neural network in order toobtain the binary classification model. The in-class regression modelgeneration module 514 performs a regression training of a machinelearning regression tool to obtain the in-class regression model. Theregression model generated based on the two algorithms are explainedherein in detail in FIG. 7.

FIG. 6A illustrates a glucose plot obtained for glucose values atdifferent time instances for generating a binary classification model,according to an exemplary embodiment. FIG. 6A also illustrates theinstances at which a glucose value of 141 mg/dL is measured, namely attimes instances 9, 116 and 122. The variation in the glucose value withrespect to time including food intake is considered. Though, the spectracorresponding to the glucose value 141 looks very similar at differenttimes, there can be small variations also. As can be seen in FIG. 6B,the spectra obtained for the same glucose value of 141 mg/dL at instant116 and 122 are highly correlated and look dissimilar to the glucosespectrum at instant 9. The difference in the nature of spectra of thesame glucose value at different instances is attributed to the nature ofthe transition of the glucose values. These transitions form the basisof multi-class classification of the spectra. Therefore, the entirerange of glucose values is classified into two bins, namely bin 1 andbin 2. Bin 1 corresponds to glucose values with a rise period and bin 2corresponds to glucose values with a decay period. The spectra belongingto the same bin are highly correlated. Based on this, a classificationmodel for the binary classification is generated. An artificial neuralnetwork (ANN) based learning is deployed for binary classification ofNIR spectra.

Now, using the two bins, two classes are defined namely, class-R andclass-D. Class-R corresponds to a raising period and class-D correspondsto a decay period. The two classes are used in creating a classificationmodel based on the ANN framework. The model training is performed usinga backpropagation algorithm implemented in MATLAB. At first, dataobtained from a test subject on the first day of an experiment,henceforth referred to as day-1 data is used for the model training. Themodel training for the ANN is performed using the backpropagationalgorithm.

In an exemplary embodiment, the trained classification model is testedusing the data obtained from the same test subject on the second day ofthe experiment, henceforth referred to as day-2 data. The features usedfor training the classification model is extracted for each of theindividual spectra in the test data-set. The extracted features areprovided as inputs to the model which makes prediction of the glucosevalues associated with individual spectra in the test data. Then, theclassification accuracy for each of the class is obtained as shown inthe below table for four different test subjects labelled as S1, S2, S3and S4. For generating these results, a three layer ANN with a hiddenlayer of 20 nodes is considered to build the classification model.

Recall True Test (True positive negative F1 Person rate) rate Precision% Accuracy score S1 0.9924 1 1 99.4 0.9961 S2 1 0.37 0.86 87.2 0.9254 S31 0.83 0.97 97.2 0.9832 S4 1 0.87 0.97 97.7 0.9865

Once the binary classification is performed, a machine learning (ML)regression model is employed for the In-class prediction. The generationof regression model is explained in FIG. 7.

FIG. 7 is a flowchart illustrating a generation of regression model,according to an exemplary embodiment. In operation 702, training data issegregated into two bins corresponding to class-R and class-D,respectively. Then regression model training is performed in eachindividual bin to predict glucose values. The regression is carried overa given feature vector that is classified into either belonging to Bin-Ror Bin-D to obtain the corresponding glucose value associated with thefeature vector. The set of features used for the in-class regressionremains the same as was used for training the classification model. Inoperation 704, the regression model is generated for each bin and inoperation 706, prediction of the glucose values is performed using thegenerated regression model for each bin. The same is illustrated in FIG.7. The accuracy results for the in-class regression are given in thetable below. A Gaussian process regression is used for the purpose ofdemonstration. The day-1 data is used for training and day-2 data isused for testing, for the respective test subjects.

Gaussian Process Regression R SEP MARD Test Bin Subject Bin 1 Bin 2R_(Exp) Bin 1 Bin 2 SEP_(Exp) Bin 1 Bin 2 MARD_(Exp) S1 0.89 0.92 0.9218.86 19.78 19.61 0.74 11.04 10.98 S2 0.86 0.81 0.82 39.84 31.88 33.5037.27 19.19 22.24 S6 0.91 0.89 0.90 8.10 8.33 8.29 4.81 5.17 5.11 S70.87 0.88 0.89 18.31 13.98 13.02 5.69 8.14 7.23

In the table, an expected correlation coefficient R_(Exp) is a weightedsummation of R values of individual bins and is defined as

$R_{Exp} = \frac{{n_{1}R_{1}} + {n_{2}R_{2}}}{n_{1} + n_{2}}$${Similarly},{{SEP}_{Exp} = \frac{{n_{1}{SEP}_{1}} + {n_{2}{SEP}_{2}}}{n_{1} + n_{2}}},{{MARD}_{Exp} = \frac{{n_{1}{MARD}_{1}} + {n_{2}{MARD}_{2}}}{n_{1} + n_{2}}}$

Here, n₁ and n₂ denote the number of samples belonging to Bin-R andBin-D, respectively

While not restricted thereto, an exemplary embodiment can be embodied ascomputer-readable code on a computer-readable recording medium. Thecomputer-readable recording medium is any data storage device that canstore data that can be thereafter read by a computer system. Examples ofthe computer-readable recording medium include read-only memory (ROM),random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, andoptical data storage devices. The computer-readable recording medium canalso be distributed over network-coupled computer systems so that thecomputer-readable code is stored and executed in a distributed fashion.Also, an exemplary embodiment may be written as a computer programtransmitted over a computer-readable transmission medium, such as acarrier wave, and received and implemented in general-use orspecial-purpose digital computers that execute the programs. Moreover,it is understood that in exemplary embodiments, one or more units of theabove-described apparatuses and devices can include circuitry, aprocessor, a microprocessor, etc., and may execute a computer programstored in a computer-readable medium.

The foregoing exemplary embodiments and advantages are merely exemplaryand are not to be construed as limiting. The present teaching can bereadily applied to other types of apparatuses. Also, the description ofthe exemplary embodiments is intended to be illustrative, and not tolimit the scope of the claims, and many alternatives, modifications, andvariations will be apparent to those skilled in the art.

What is claimed is:
 1. A method of predicting a blood glucose levelusing near-infrared (NIR) spectroscopy data, the method comprising:obtaining a feature set from an NIR glucose spectra; and predictingglucose values from the feature set based on a binary classification ofthe NIR glucose spectra and an in-class prediction of glucose usingMachine Learning Regression, wherein the obtaining the feature setcomprises: obtaining a raw feature set from NIR glucose spectra samplesassociated with different blood glucose levels; identifying one or moreglucose dependent features present in the NIR glucose spectra samples;and removing collinearity from the identified one or more glucosedependent features to obtain the feature set.
 2. The method as claimedin claim 1, wherein the identifying one or more glucose dependentfeatures present in the NIR glucose spectra samples comprises: obtaininga low variance set of features that exhibits a low variance for a sameglucose value in the NIR glucose spectra; obtaining a high variance setof features that varies in accordance with a change in glucose levels;and obtaining the one or more glucose dependent features as featureswhich are common to both of the low variance set and the high varianceset.
 3. The method as claimed in claim 1, wherein the predicting theglucose values comprises: obtaining a glucose plot representing aplurality of glucose values at different time instances; and classifyingthe plurality of glucose values based on the time instances into a firstbin and a second bin, wherein the first bin corresponds to glucosevalues with a rise time period and the second bin corresponds to glucosevalues with a decay time period.
 4. The method as claimed in claim 3,further comprising: generating an artificial neural network (ANN)framework based a classification model that classifies the NIR glucosespectra into the first bin and the second bin using a backpropagationmethod; and classifying the NIR glucose spectra based on the generatedclassification model.
 5. The method as claimed in claim 1, wherein thepredicting the glucose values comprises: segregating the NIRspectroscopy data into one or more binary classes; generating aregression model for each of the one or more binary classes; andpredicting the glucose values based on the generated regression modelfor each class.
 6. A glucose prediction device comprising at least oneor more processors, the at least one or more processors comprising: afeature set extraction unit configured to obtain a feature set from anNIR glucose spectra; and a prediction unit configured to predict glucosevalues from the feature set based on a binary classification of the NIRglucose spectra and an in-class prediction of glucose using MachineLearning Regression, wherein the feature set extraction unit comprises:a raw feature extraction unit configured to extract a raw feature setfrom NIR glucose spectra samples associated with different blood glucoselevels; a glucose dependent features isolation unit configured toidentify one or more glucose dependent features present in the NIRglucose spectra samples; and a collinearity removal unit configured toremove collinearity from the identified one or more glucose dependentfeatures to obtain the feature set.
 7. The glucose prediction device ofclaim 6, wherein the glucose dependent features isolation unit isfurther configured to obtain a low variance set of features thatrepresents a low variance for a same glucose value in the NIR glucosespectra, obtain a high variance set of features that varies inaccordance with a change in glucose levels, and obtain the one or moreglucose dependent features as features which are common to both of thelow variance set and the high variance set.